library(survival)
library(survminer)
library(dplyr)
setwd("M:/Elena - R/Multi-state  cancer")
load("1 data prep/working.data.Rda")
# to all variable in the environment, group age, select complete cases, group obesity

e<-eapply(.GlobalEnv, function(x) {
  x<-x%>%
 mutate(age_gr.b= ifelse(age<=59, "Aged 18 to 59",
                            ifelse(age<=79, "Aged 60 to 79",
                                   "Aged 80 or above" ))) %>%
    mutate(age_gr.b = factor(age_gr.b, levels = c("Aged 18 to 59", "Aged 60 to 79","Aged 80 or above"))) %>%
    mutate(age_2gr= ifelse(age<70, "Aged 18 to 69",
                            "Aged 70 or above")) %>%
    mutate(age_2gr= factor(age_2gr, levels = c("Aged 18 to 69", "Aged 70 or above"))) %>%
    mutate(smoke.all_time=factor(smoke.all_time))%>%
    mutate(smoke.all_time=relevel(smoke.all_time, "Never smoker"))%>%
    mutate(medea=relevel(medea, "U1"))%>%
    droplevels()
  output <- x
}
)

nms <- names(e)
for ( n in nms ){
  assign(n, e[[n]])
}

Graphical check proportionality of variables for the cox model

Transition 1: From general population to diagnosed with COVID-19

Prior diagnosis of cancer

Cancer yes/no

survfit.t1.cancer <- survfit(Surv(time, status) ~ cancer, 
                      data = r.healthy.diagnosis)
ggsurvplot.event<-ggsurvplot(survfit.t1.cancer, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())


ggsurvplot.cloglog<-ggsurvplot(survfit.t1.cancer, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
                          theme(legend.position = "none") 
  
survfit.t1.cancer<- list(ggsurvplot.event, ggsurvplot.cloglog)
survfit.t1.cancer <-arrange_ggsurvplots(survfit.t1.cancer, print = FALSE, ncol = 1, nrow = 2)
survfit.t1.cancer

Years since cancer diagnosis

survfit.t1.c_status <- survfit(Surv(time, status) ~ c_status, 
                      data = r.healthy.diagnosis)

ggsurvplot.event<-ggsurvplot(survfit.t1.c_status, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())

ggsurvplot.cloglog<-ggsurvplot(survfit.t1.c_status, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,100))+
  theme(legend.title = element_blank())
  
survfit.t1.c_status<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
ggsurvplot.cloglog

survfit.t1.c_status <-arrange_ggsurvplots(survfit.t1.c_status, print = FALSE, ncol = 1, nrow = 2)
survfit.t1.c_status

Hematological vs solid

survfit.t1.hem_solid <- survfit(Surv(time, status) ~ hem_solid, 
                      data = r.healthy.diagnosis)

ggsurvplot.event<-ggsurvplot(survfit.t1.hem_solid, fun = "event", conf.int =TRUE)

ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())

ggsurvplot.cloglog<-ggsurvplot(survfit.t1.hem_solid, fun = "cloglog", conf.int =TRUE)

ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
                          theme(legend.position = "none") 
  

survfit.t1.hem_solid<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
survfit.t1.hem_solid <-arrange_ggsurvplots(survfit.t1.hem_solid, print = FALSE, ncol = 1, nrow = 2)
survfit.t1.hem_solid

Gender

survfit.t1.gender<-survfit(Surv(time, status) ~ gender, 
                 data = r.healthy.diagnosis)

ggsurvplot.event<-ggsurvplot(survfit.t1.gender, fun = "event", conf.int =TRUE)

ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())

ggsurvplot.cloglog<-ggsurvplot(survfit.t1.gender, fun = "cloglog", conf.int =TRUE)

ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.position = "none") 
  
ggsurv.t1.gender<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)

ggsurv.t1.gender <-arrange_ggsurvplots(ggsurv.t1.gender, print = FALSE, ncol = 1, nrow = 2)

ggsurv.t1.gender 

Age groups

survfit.t1.age_gr<-survfit(Surv(time, status) ~ age_gr.b, 
                 data = r.healthy.diagnosis)

ggsurv.t1.age_gr<- list(
  ggsurvplot(survfit.t1.age_gr, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t1.age_gr, fun = "cloglog", conf.int =TRUE))

ggsurv.t1.age_gr <-arrange_ggsurvplots(ggsurv.t1.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.age_gr

survfit.t1.age_2gr<-survfit(Surv(time, status) ~ age_2gr, 
                 data = r.healthy.diagnosis)

ggsurv.t1.age_2gr<- list(
  ggsurvplot(survfit.t1.age_2gr, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t1.age_2gr, fun = "cloglog", conf.int =TRUE))

ggsurv.t1.age_2gr <-arrange_ggsurvplots(ggsurv.t1.age_2gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.age_2gr

Smoke

survfit.t1.smoke<-survfit(Surv(time, status) ~ smoke.all_time, 
                 data = r.healthy.diagnosis)

ggsurv.t1.smoke<- list(
  ggsurvplot(survfit.t1.smoke, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t1.smoke, fun = "cloglog", conf.int =TRUE))

ggsurv.t1.smoke <-arrange_ggsurvplots(ggsurv.t1.smoke, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.smoke

Medea

survfit.t1.medea<-survfit(Surv(time, status) ~ medea, 
                 data = r.healthy.diagnosis)

ggsurv.t1.medea<- list(
  ggsurvplot(survfit.t1.medea, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t1.medea, fun = "cloglog", conf.int =TRUE))

ggsurv.t1.medea <-arrange_ggsurvplots(ggsurv.t1.medea, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.medea

Comorbidities

Autoinmune condition

survfit.t1.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition, 
                 data = r.healthy.diagnosis)
ggsurv.t1.a_autoimmune_condition<- list(
  ggsurvplot(survfit.t1.a_autoimmune_condition, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t1.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t1.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_autoimmune_condition 

Chronic Kidney Disease

survfit.t1.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease, 
                 data = r.healthy.diagnosis)
ggsurv.t1.a_chronic_kidney_disease<- list(
  ggsurvplot(survfit.t1.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t1.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t1.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_chronic_kidney_disease

COPD

survfit.t1.a_copd<-survfit(Surv(time, status) ~ a_copd, 
                 data = r.healthy.diagnosis)
ggsurv.t1.a_copd<- list(
  ggsurvplot(survfit.t1.a_copd, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t1.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_copd <-arrange_ggsurvplots(ggsurv.t1.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_copd

Dementia

survfit.t1.a_dementia<-survfit(Surv(time, status) ~ a_dementia , 
                 data = r.healthy.diagnosis)
ggsurv.t1.a_dementia<- list(
  ggsurvplot(survfit.t1.a_dementia, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t1.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_dementia <-arrange_ggsurvplots(ggsurv.t1.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_dementia

Heart disease

survfit.t1.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease, 
                 data = r.healthy.diagnosis)
ggsurv.t1.a_heart_disease<- list(
  ggsurvplot(survfit.t1.a_heart_disease, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t1.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_heart_disease <-arrange_ggsurvplots(ggsurv.t1.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_heart_disease

Hyperlipidemia

survfit.t1.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia, 
                 data = r.healthy.diagnosis)
ggsurv.t1.a_hyperlipidemia<- list(
  ggsurvplot(survfit.t1.a_hyperlipidemia, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t1.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t1.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_hyperlipidemia

Hypertension

survfit.t1.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension, 
                 data = r.healthy.diagnosis)
ggsurv.t1.a_hypertension<- list(
  ggsurvplot(survfit.t1.a_hypertension, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t1.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_hypertension <-arrange_ggsurvplots(ggsurv.t1.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_hypertension

Obesity

survfit.t1.a_obesity<-survfit(Surv(time, status) ~ a_obesity, 
                 data = r.healthy.diagnosis)
ggsurv.t1.a_obesity<- list(
  ggsurvplot(survfit.t1.a_obesity, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t1.a_obesity, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_obesity <-arrange_ggsurvplots(ggsurv.t1.a_obesity, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_obesity

Type 2 Diabetes

survfit.t1.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes, 
                 data = r.healthy.diagnosis)
ggsurv.t1.a_t2_diabetes<- list(
  ggsurvplot(survfit.t1.a_t2_diabetes, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t1.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t1.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t1.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t1.a_t2_diabetes

Transition 2: From general population to hospitalised with COVID-19

Prior diagnosis of cancer

Cancer yes /no

survfit.t2.cancer <- survfit(Surv(time, status) ~ cancer, 
                      data = r.healthy.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t2.cancer, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t2.cancer, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
                          theme(legend.position = "none") 
  
survfit.t2.cancer<- list(ggsurvplot.event, ggsurvplot.cloglog)
survfit.t2.cancer <-arrange_ggsurvplots(survfit.t2.cancer, print = FALSE, ncol = 1, nrow = 2)
survfit.t2.cancer

Years since cancer diagnosis

survfit.t2.c_status <- survfit(Surv(time, status) ~ c_status, 
                      data = r.healthy.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t2.c_status, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t2.c_status, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
                          theme(legend.position = "none") 
  
survfit.t2.c_status<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
survfit.t2.c_status <-arrange_ggsurvplots(survfit.t2.c_status, print = FALSE, ncol = 1, nrow = 2)
survfit.t2.c_status

Cancer type, hematological or solid vs cancer-free

survfit.t2.hem_solid <- survfit(Surv(time, status) ~ hem_solid, 
                      data = r.healthy.hospitalised)

ggsurvplot.event<-ggsurvplot(survfit.t2.hem_solid, fun = "event", conf.int =TRUE)

ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())

ggsurvplot.cloglog<-ggsurvplot(survfit.t2.hem_solid, fun = "cloglog", conf.int =TRUE)

ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
                          theme(legend.position = "none") 
  

survfit.t2.hem_solid<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
survfit.t2.hem_solid <-arrange_ggsurvplots(survfit.t2.hem_solid, print = FALSE, ncol = 1, nrow = 2)
survfit.t2.hem_solid

Gender

survfit.t2.gender<-survfit(Surv(time, status) ~ gender, 
                 data = r.healthy.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t2.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t2.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.position = "none") 
  

ggsurv.t2.gender<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
ggsurv.t2.gender <-arrange_ggsurvplots(ggsurv.t2.gender, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.gender 

Age groups

survfit.t2.age_gr<-survfit(Surv(time, status) ~ age_gr.b, 
                 data = r.healthy.hospitalised)
ggsurv.t2.age_gr<- list(
  ggsurvplot(survfit.t2.age_gr, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t2.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.age_gr <-arrange_ggsurvplots(ggsurv.t2.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.age_gr

Smoke

survfit.t2.smoke<-survfit(Surv(time, status) ~ smoke.all_time, 
                 data = r.healthy.hospitalised)
ggsurv.t2.smoke<- list(
  ggsurvplot(survfit.t2.smoke, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t2.smoke, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.smoke <-arrange_ggsurvplots(ggsurv.t2.smoke, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.smoke

Medea

survfit.t2.medea<-survfit(Surv(time, status) ~ medea, 
                 data = r.healthy.hospitalised)
ggsurv.t2.medea<- list(
  ggsurvplot(survfit.t2.medea, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t2.medea, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.medea <-arrange_ggsurvplots(ggsurv.t2.medea, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.medea

Comorbidities

Autoinmune condition

survfit.t2.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition, 
                 data = r.healthy.hospitalised)
ggsurv.t2.a_autoimmune_condition<- list(
  ggsurvplot(survfit.t2.a_autoimmune_condition, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t2.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t2.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_autoimmune_condition 

Chronic Kidney Disease

survfit.t2.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease, 
                 data = r.healthy.hospitalised)
ggsurv.t2.a_chronic_kidney_disease<- list(
  ggsurvplot(survfit.t2.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t2.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t2.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_chronic_kidney_disease

COPD

survfit.t2.a_copd<-survfit(Surv(time, status) ~ a_copd, 
                 data = r.healthy.hospitalised)
ggsurv.t2.a_copd<- list(
  ggsurvplot(survfit.t2.a_copd, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t2.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_copd <-arrange_ggsurvplots(ggsurv.t2.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_copd

Dementia

survfit.t2.a_dementia<-survfit(Surv(time, status) ~ a_dementia, 
                 data = r.healthy.hospitalised)
ggsurv.t2.a_dementia<- list(
  ggsurvplot(survfit.t2.a_dementia, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t2.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_dementia <-arrange_ggsurvplots(ggsurv.t2.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_dementia

Heart disease

survfit.t2.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease, 
                 data = r.healthy.hospitalised)
ggsurv.t2.a_heart_disease<- list(
  ggsurvplot(survfit.t2.a_heart_disease, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t2.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_heart_disease <-arrange_ggsurvplots(ggsurv.t2.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_heart_disease

Hyperlipidemia

survfit.t2.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia, 
                 data = r.healthy.hospitalised)
ggsurv.t2.a_hyperlipidemia<- list(
  ggsurvplot(survfit.t2.a_hyperlipidemia, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t2.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t2.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_hyperlipidemia

Hypertension

survfit.t2.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension, 
                 data = r.healthy.hospitalised)
ggsurv.t2.a_hypertension<- list(
  ggsurvplot(survfit.t2.a_hypertension, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t2.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_hypertension <-arrange_ggsurvplots(ggsurv.t2.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_hypertension

Obesity

survfit.t2.a_obesity<-survfit(Surv(time, status) ~ a_obesity, 
                 data = r.healthy.hospitalised)
ggsurv.t2.a_obesity<- list(
  ggsurvplot(survfit.t2.a_obesity, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t2.a_obesity, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_obesity <-arrange_ggsurvplots(ggsurv.t2.a_obesity, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_obesity

Type 2 DM

survfit.t2.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes, 
                 data = r.healthy.hospitalised)
ggsurv.t2.a_t2_diabetes<- list(
  ggsurvplot(survfit.t2.a_t2_diabetes, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t2.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t2.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t2.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t2.a_t2_diabetes

Transition 3: From general population to death

Prior diagnosis of cancer

Cancer yes/no

survfit.t3.cancer <- survfit(Surv(time, status) ~ cancer, 
                      data = r.healthy.death)
ggsurvplot.event<-ggsurvplot(survfit.t3.cancer, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t3.cancer, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
                          theme(legend.position = "none") 
  
survfit.t3.cancer<- list(ggsurvplot.event, ggsurvplot.cloglog)
survfit.t3.cancer <-arrange_ggsurvplots(survfit.t3.cancer, print = FALSE, ncol = 1, nrow = 2)
survfit.t3.cancer

Years since cancer diagnosis

#graphical check proportionality of variables for the cox model

survfit.t3.c_status<-survfit(Surv(time, status) ~ c_status, 
                 data = r.healthy.death)
ggsurvplot.event<-ggsurvplot(survfit.t3.c_status, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t3.c_status, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.position = "none") 
  

survfit.t3.c_status<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
survfit.t3.c_status <-arrange_ggsurvplots(survfit.t3.c_status, print = FALSE, ncol = 1, nrow = 2)
survfit.t3.c_status

Cancer type, hematological or solid vs cancer-free

survfit.t3.hem_solid <- survfit(Surv(time, status) ~ hem_solid, 
                      data = r.healthy.death)

ggsurvplot.event<-ggsurvplot(survfit.t3.hem_solid, fun = "event", conf.int =TRUE)

ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())

ggsurvplot.cloglog<-ggsurvplot(survfit.t3.hem_solid, fun = "cloglog", conf.int =TRUE)

ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
                          theme(legend.position = "none") 
  

survfit.t3.hem_solid<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
survfit.t3.hem_solid <-arrange_ggsurvplots(survfit.t3.hem_solid, print = FALSE, ncol = 1, nrow = 2)
survfit.t3.hem_solid

Gender

survfit.t3.gender<-survfit(Surv(time, status) ~ gender, 
                 data = r.healthy.death)
ggsurvplot.event<-ggsurvplot(survfit.t3.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t3.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.position = "none") 
  

ggsurv.t3.gender<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
ggsurv.t3.gender <-arrange_ggsurvplots(ggsurv.t3.gender, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.gender 

Age groups

survfit.t3.age_gr<-survfit(Surv(time, status) ~ age_gr.b, 
                 data = r.healthy.death)
ggsurv.t3.age_gr<- list(
  ggsurvplot(survfit.t3.age_gr, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t3.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.age_gr <-arrange_ggsurvplots(ggsurv.t3.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.age_gr

Smoke

survfit.t3.smoke<-survfit(Surv(time, status) ~ smoke.all_time, 
                 data = r.healthy.death)
ggsurv.t3.smoke<- list(
  ggsurvplot(survfit.t3.smoke, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t3.smoke, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.smoke <-arrange_ggsurvplots(ggsurv.t3.smoke, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.smoke

Medea

survfit.t3.medea<-survfit(Surv(time, status) ~ medea, 
                 data = r.healthy.death)
ggsurv.t3.medea<- list(
  ggsurvplot(survfit.t3.medea, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t3.medea, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.medea <-arrange_ggsurvplots(ggsurv.t3.medea, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.medea

Comorbidities

Autoinmune condition

survfit.t3.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition, 
                 data = r.healthy.death)
ggsurv.t3.a_autoimmune_condition<- list(
  ggsurvplot(survfit.t3.a_autoimmune_condition, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t3.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t3.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_autoimmune_condition 

Chronic Kidney Disease

survfit.t3.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease, 
                 data = r.healthy.death)
ggsurv.t3.a_chronic_kidney_disease<- list(
  ggsurvplot(survfit.t3.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t3.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t3.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_chronic_kidney_disease

COPD

survfit.t3.a_copd<-survfit(Surv(time, status) ~ a_copd, 
                 data = r.healthy.death)
ggsurv.t3.a_copd<- list(
  ggsurvplot(survfit.t3.a_copd, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t3.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_copd <-arrange_ggsurvplots(ggsurv.t3.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_copd

Dementia

survfit.t3.a_dementia<-survfit(Surv(time, status) ~ a_dementia, 
                 data = r.healthy.death)
ggsurv.t3.a_dementia<- list(
  ggsurvplot(survfit.t3.a_dementia, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t3.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_dementia <-arrange_ggsurvplots(ggsurv.t3.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_dementia

Heart disease

survfit.t3.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease, 
                 data = r.healthy.death)
ggsurv.t3.a_heart_disease<- list(
  ggsurvplot(survfit.t3.a_heart_disease, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t3.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_heart_disease <-arrange_ggsurvplots(ggsurv.t3.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_heart_disease

Hyperlipidemia

survfit.t3.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia, 
                 data = r.healthy.death)
ggsurv.t3.a_hyperlipidemia<- list(
  ggsurvplot(survfit.t3.a_hyperlipidemia, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t3.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t3.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_hyperlipidemia

Hypertension

survfit.t3.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension, 
                 data = r.healthy.death)
ggsurv.t3.a_hypertension<- list(
  ggsurvplot(survfit.t3.a_hypertension, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t3.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_hypertension <-arrange_ggsurvplots(ggsurv.t3.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_hypertension

Obesity

survfit.t3.a_obesity<-survfit(Surv(time, status) ~ a_obesity, 
                 data = r.healthy.death)
ggsurv.t3.a_obesity<- list(
  ggsurvplot(survfit.t3.a_obesity, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t3.a_obesity, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_obesity <-arrange_ggsurvplots(ggsurv.t3.a_obesity, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_obesity

Type 2 DM

survfit.t3.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes, 
                 data = r.healthy.death)
ggsurv.t3.a_t2_diabetes<- list(
  ggsurvplot(survfit.t3.a_t2_diabetes, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t3.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t3.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t3.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t3.a_t2_diabetes

Transition 4: From diagnosed with COVID-19 to hospitalised with COVID-19

Prior diagnosis of cancer

cancer yes/no

survfit.t4.cancer <- survfit(Surv(time, status) ~ cancer, 
                      data = r.diagnosis.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t4.cancer, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t4.cancer, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
                          theme(legend.position = "none") 
  
survfit.t4.cancer<- list(ggsurvplot.event, ggsurvplot.cloglog)
survfit.t4.cancer <-arrange_ggsurvplots(survfit.t4.cancer, print = FALSE, ncol = 1, nrow = 2)
survfit.t4.cancer

Years since cancer diagnosis

#graphical check proportionality of variables for the cox model

survfit.t4.c_status<-survfit(Surv(time, status) ~ c_status, 
                 data = r.diagnosis.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t4.c_status, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t4.c_status, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.position = "none") 
  

survfit.t4.c_status<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
survfit.t4.c_status <-arrange_ggsurvplots(survfit.t4.c_status, print = FALSE, ncol = 1, nrow = 2)
survfit.t4.c_status

Cancer type, hematological or solid vs cancer-free

survfit.t4.hem_solid <- survfit(Surv(time, status) ~ hem_solid, 
                      data = r.diagnosis.hospitalised)

ggsurvplot.event<-ggsurvplot(survfit.t4.hem_solid, fun = "event", conf.int =TRUE)

ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())

ggsurvplot.cloglog<-ggsurvplot(survfit.t4.hem_solid, fun = "cloglog", conf.int =TRUE)

ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
                          theme(legend.position = "none") 
  

survfit.t4.hem_solid<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
survfit.t4.hem_solid <-arrange_ggsurvplots(survfit.t4.hem_solid, print = FALSE, ncol = 1, nrow = 2)
survfit.t4.hem_solid

Gender

survfit.t4.gender<-survfit(Surv(time, status) ~ gender, 
                 data = r.diagnosis.hospitalised)
ggsurvplot.event<-ggsurvplot(survfit.t4.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t4.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.position = "none") 
  

ggsurv.t4.gender<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
ggsurv.t4.gender <-arrange_ggsurvplots(ggsurv.t4.gender, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.gender 

Age groups

survfit.t4.age_gr<-survfit(Surv(time, status) ~ age_gr.b, 
                 data = r.diagnosis.hospitalised)
ggsurv.t4.age_gr<- list(
  ggsurvplot(survfit.t4.age_gr, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t4.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.age_gr <-arrange_ggsurvplots(ggsurv.t4.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.age_gr

Smoke

survfit.t4.smoke<-survfit(Surv(time, status) ~ smoke.all_time, 
                 data = r.diagnosis.hospitalised)
ggsurv.t4.smoke<- list(
  ggsurvplot(survfit.t4.smoke, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t4.smoke, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.smoke <-arrange_ggsurvplots(ggsurv.t4.smoke, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.smoke

Medea

survfit.t4.medea<-survfit(Surv(time, status) ~ medea, 
                 data = r.diagnosis.hospitalised)
ggsurv.t4.medea<- list(
  ggsurvplot(survfit.t4.medea, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t4.medea, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.medea <-arrange_ggsurvplots(ggsurv.t4.medea, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.medea

Comorbidities

Autoinmune condition

survfit.t4.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition, 
                 data = r.diagnosis.hospitalised)
ggsurv.t4.a_autoimmune_condition<- list(
  ggsurvplot(survfit.t4.a_autoimmune_condition, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t4.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t4.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_autoimmune_condition 

Chronic Kidney Disease

survfit.t4.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease, 
                 data = r.diagnosis.hospitalised)
ggsurv.t4.a_chronic_kidney_disease<- list(
  ggsurvplot(survfit.t4.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t4.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t4.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_chronic_kidney_disease

COPD

survfit.t4.a_copd<-survfit(Surv(time, status) ~ a_copd, 
                 data = r.diagnosis.hospitalised)
ggsurv.t4.a_copd<- list(
  ggsurvplot(survfit.t4.a_copd, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t4.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_copd <-arrange_ggsurvplots(ggsurv.t4.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_copd

Dementia

survfit.t4.a_dementia<-survfit(Surv(time, status) ~ a_dementia, 
                 data = r.diagnosis.hospitalised)
ggsurv.t4.a_dementia<- list(
  ggsurvplot(survfit.t4.a_dementia, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t4.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_dementia <-arrange_ggsurvplots(ggsurv.t4.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_dementia

Heart disease

survfit.t4.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease, 
                 data = r.diagnosis.hospitalised)
ggsurv.t4.a_heart_disease<- list(
  ggsurvplot(survfit.t4.a_heart_disease, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t4.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_heart_disease <-arrange_ggsurvplots(ggsurv.t4.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_heart_disease

Hyperlipidemia

survfit.t4.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia, 
                 data = r.diagnosis.hospitalised)
ggsurv.t4.a_hyperlipidemia<- list(
  ggsurvplot(survfit.t4.a_hyperlipidemia, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t4.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t4.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_hyperlipidemia

Hypertension

survfit.t4.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension, 
                 data = r.diagnosis.hospitalised)
ggsurv.t4.a_hypertension<- list(
  ggsurvplot(survfit.t4.a_hypertension, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t4.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_hypertension <-arrange_ggsurvplots(ggsurv.t4.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_hypertension

Obesity

survfit.t4.a_obesity<-survfit(Surv(time, status) ~ a_obesity, 
                 data = r.diagnosis.hospitalised)
ggsurv.t4.a_obesity<- list(
  ggsurvplot(survfit.t4.a_obesity, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t4.a_obesity, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_obesity <-arrange_ggsurvplots(ggsurv.t4.a_obesity, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_obesity

Type 2 DM

survfit.t4.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes, 
                 data = r.diagnosis.hospitalised)
ggsurv.t4.a_t2_diabetes<- list(
  ggsurvplot(survfit.t4.a_t2_diabetes, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t4.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t4.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t4.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t4.a_t2_diabetes

Transition 5: From diagnosed with COVID-19 to death

Prior diagnosis of cancer

cancer yes/no

survfit.t5.cancer <- survfit(Surv(time, status) ~ cancer, 
                      data = r.diagnosis.death)
ggsurvplot.event<-ggsurvplot(survfit.t5.cancer, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t5.cancer, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
                          theme(legend.position = "none") 
  
survfit.t5.cancer<- list(ggsurvplot.event, ggsurvplot.cloglog)
survfit.t5.cancer <-arrange_ggsurvplots(survfit.t5.cancer, print = FALSE, ncol = 1, nrow = 2)
survfit.t5.cancer

Years since cancer diagnosis

#graphical check proportionality of variables for the cox model

survfit.t5.c_status<-survfit(Surv(time, status) ~ c_status, 
                 data = r.diagnosis.death)
ggsurvplot.event<-ggsurvplot(survfit.t5.c_status, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t5.c_status, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.position = "none") 
  

survfit.t5.c_status<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
survfit.t5.c_status <-arrange_ggsurvplots(survfit.t5.c_status, print = FALSE, ncol = 1, nrow = 2)
survfit.t5.c_status

Cancer type, hematological or solid vs cancer-free

survfit.t5.hem_solid <- survfit(Surv(time, status) ~ hem_solid, 
                      data = r.diagnosis.death)

ggsurvplot.event<-ggsurvplot(survfit.t5.hem_solid, fun = "event", conf.int =TRUE)

ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())

ggsurvplot.cloglog<-ggsurvplot(survfit.t5.hem_solid, fun = "cloglog", conf.int =TRUE)

ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
                          theme(legend.position = "none") 
  

survfit.t5.hem_solid<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
survfit.t5.hem_solid <-arrange_ggsurvplots(survfit.t5.hem_solid, print = FALSE, ncol = 1, nrow = 2)
survfit.t5.hem_solid

Gender

survfit.t5.gender<-survfit(Surv(time, status) ~ gender, 
                 data = r.diagnosis.death)
ggsurvplot.event<-ggsurvplot(survfit.t5.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t5.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.position = "none") 
  

ggsurv.t5.gender<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
ggsurv.t5.gender <-arrange_ggsurvplots(ggsurv.t5.gender, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.gender 

Age groups

survfit.t5.age_gr<-survfit(Surv(time, status) ~ age_gr.b, 
                 data = r.diagnosis.death)
ggsurv.t5.age_gr<- list(
  ggsurvplot(survfit.t5.age_gr, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t5.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.age_gr <-arrange_ggsurvplots(ggsurv.t5.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.age_gr

Smoke

survfit.t5.smoke<-survfit(Surv(time, status) ~ smoke.all_time, 
                 data = r.diagnosis.death)
ggsurv.t5.smoke<- list(
  ggsurvplot(survfit.t5.smoke, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t5.smoke, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.smoke <-arrange_ggsurvplots(ggsurv.t5.smoke, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.smoke

Medea

survfit.t5.medea<-survfit(Surv(time, status) ~ medea, 
                 data = r.diagnosis.death)
ggsurv.t5.medea<- list(
  ggsurvplot(survfit.t5.medea, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t5.medea, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.medea <-arrange_ggsurvplots(ggsurv.t5.medea, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.medea

Comorbidities

Autoinmune condition

survfit.t5.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition, 
                 data = r.diagnosis.death)
ggsurv.t5.a_autoimmune_condition<- list(
  ggsurvplot(survfit.t5.a_autoimmune_condition, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t5.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t5.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_autoimmune_condition 

Chronic Kidney Disease

survfit.t5.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease, 
                 data = r.diagnosis.death)
ggsurv.t5.a_chronic_kidney_disease<- list(
  ggsurvplot(survfit.t5.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t5.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t5.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_chronic_kidney_disease

COPD

survfit.t5.a_copd<-survfit(Surv(time, status) ~ a_copd, 
                 data = r.diagnosis.death)
ggsurv.t5.a_copd<- list(
  ggsurvplot(survfit.t5.a_copd, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t5.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_copd <-arrange_ggsurvplots(ggsurv.t5.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_copd

Dementia

survfit.t5.a_dementia<-survfit(Surv(time, status) ~ a_dementia, 
                 data = r.diagnosis.death)
ggsurv.t5.a_dementia<- list(
  ggsurvplot(survfit.t5.a_dementia, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t5.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_dementia <-arrange_ggsurvplots(ggsurv.t5.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_dementia

Heart disease

survfit.t5.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease, 
                 data = r.diagnosis.death)
ggsurv.t5.a_heart_disease<- list(
  ggsurvplot(survfit.t5.a_heart_disease, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t5.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_heart_disease <-arrange_ggsurvplots(ggsurv.t5.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_heart_disease

Hyperlipidemia

survfit.t5.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia, 
                 data = r.diagnosis.death)
ggsurv.t5.a_hyperlipidemia<- list(
  ggsurvplot(survfit.t5.a_hyperlipidemia, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t5.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t5.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_hyperlipidemia

Hypertension

survfit.t5.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension, 
                 data = r.diagnosis.death)
ggsurv.t5.a_hypertension<- list(
  ggsurvplot(survfit.t5.a_hypertension, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t5.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_hypertension <-arrange_ggsurvplots(ggsurv.t5.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_hypertension

Obesity

survfit.t5.a_obesity<-survfit(Surv(time, status) ~ a_obesity, 
                 data = r.diagnosis.death)
ggsurv.t5.a_obesity<- list(
  ggsurvplot(survfit.t5.a_obesity, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t5.a_obesity, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_obesity <-arrange_ggsurvplots(ggsurv.t5.a_obesity, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_obesity

Type 2 DM

survfit.t5.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes, 
                 data = r.diagnosis.death)
ggsurv.t5.a_t2_diabetes<- list(
  ggsurvplot(survfit.t5.a_t2_diabetes, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t5.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t5.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t5.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t5.a_t2_diabetes

Transition 6: From hospitalised with COVID-19 to death

Prior diagnosis of cancer

cancer yes/no

survfit.t6.cancer <- survfit(Surv(time, status) ~ cancer, 
                      data = r.hospitalised.death)
ggsurvplot.event<-ggsurvplot(survfit.t6.cancer, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t6.cancer, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
                          theme(legend.position = "none") 
  
survfit.t6.cancer<- list(ggsurvplot.event, ggsurvplot.cloglog)
survfit.t6.cancer <-arrange_ggsurvplots(survfit.t6.cancer, print = FALSE, ncol = 1, nrow = 2)
survfit.t6.cancer

Years since cancer diagnosis

survfit.t6.c_status<-survfit(Surv(time, status) ~ c_status, 
                 data = r.hospitalised.death)
ggsurvplot.event<-ggsurvplot(survfit.t6.c_status, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t6.c_status, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.position = "none") 
  

survfit.t6.c_status<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
survfit.t6.c_status <-arrange_ggsurvplots(survfit.t6.c_status, print = FALSE, ncol = 1, nrow = 2)
survfit.t6.c_status

Cancer type, hematological or solid vs cancer-free

survfit.t6.hem_solid <- survfit(Surv(time, status) ~ hem_solid, 
                      data = r.hospitalised.death)

ggsurvplot.event<-ggsurvplot(survfit.t6.hem_solid, fun = "event", conf.int =TRUE)

ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())

ggsurvplot.cloglog<-ggsurvplot(survfit.t6.hem_solid, fun = "cloglog", conf.int =TRUE)

ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
                          theme(legend.position = "none") 
  

survfit.t6.hem_solid<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
survfit.t6.hem_solid <-arrange_ggsurvplots(survfit.t6.hem_solid, print = FALSE, ncol = 1, nrow = 2)
survfit.t6.hem_solid

Gender

survfit.t6.gender<-survfit(Surv(time, status) ~ gender, 
                 data = r.hospitalised.death)
ggsurvplot.event<-ggsurvplot(survfit.t6.gender, fun = "event", conf.int =TRUE)
ggsurvplot.event$plot<-ggsurvplot.event$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.title = element_blank())
ggsurvplot.cloglog<-ggsurvplot(survfit.t6.gender, fun = "cloglog", conf.int =TRUE)
ggsurvplot.cloglog$plot<-ggsurvplot.cloglog$plot+ coord_cartesian(xlim = c(NA,67))+
  theme(legend.position = "none") 
  

ggsurv.t6.gender<- list(
  ggsurvplot.event,
  ggsurvplot.cloglog)
ggsurv.t6.gender <-arrange_ggsurvplots(ggsurv.t6.gender, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.gender 

Age groups

survfit.t6.age_gr<-survfit(Surv(time, status) ~ age_gr.b, 
                 data = r.hospitalised.death)
ggsurv.t6.age_gr<- list(
  ggsurvplot(survfit.t6.age_gr, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t6.age_gr, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.age_gr <-arrange_ggsurvplots(ggsurv.t6.age_gr, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.age_gr

Smoke

survfit.t6.smoke<-survfit(Surv(time, status) ~ smoke.all_time, 
                 data = r.hospitalised.death)
ggsurv.t6.smoke<- list(
  ggsurvplot(survfit.t6.smoke, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t6.smoke, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.smoke <-arrange_ggsurvplots(ggsurv.t6.smoke, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.smoke

Medea

survfit.t6.medea<-survfit(Surv(time, status) ~ medea, 
                 data = r.hospitalised.death)
ggsurv.t6.medea<- list(
  ggsurvplot(survfit.t6.medea, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t6.medea, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.medea <-arrange_ggsurvplots(ggsurv.t6.medea, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.medea

Comorbidities

Autoinmune condition

survfit.t6.a_autoimmune_condition<-survfit(Surv(time, status) ~ a_autoimmune_condition, 
                 data = r.hospitalised.death)
ggsurv.t6.a_autoimmune_condition<- list(
  ggsurvplot(survfit.t6.a_autoimmune_condition, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t6.a_autoimmune_condition, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_autoimmune_condition <-arrange_ggsurvplots(ggsurv.t6.a_autoimmune_condition, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_autoimmune_condition 

Chronic Kidney Disease

survfit.t6.a_chronic_kidney_disease<-survfit(Surv(time, status) ~ a_chronic_kidney_disease, 
                 data = r.hospitalised.death)
ggsurv.t6.a_chronic_kidney_disease<- list(
  ggsurvplot(survfit.t6.a_chronic_kidney_disease, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t6.a_chronic_kidney_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_chronic_kidney_disease <-arrange_ggsurvplots(ggsurv.t6.a_chronic_kidney_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_chronic_kidney_disease

COPD

survfit.t6.a_copd<-survfit(Surv(time, status) ~ a_copd, 
                 data = r.hospitalised.death)
ggsurv.t6.a_copd<- list(
  ggsurvplot(survfit.t6.a_copd, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t6.a_copd, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_copd <-arrange_ggsurvplots(ggsurv.t6.a_copd, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_copd

Dementia

survfit.t6.a_dementia<-survfit(Surv(time, status) ~ a_dementia, 
                 data = r.hospitalised.death)
ggsurv.t6.a_dementia<- list(
  ggsurvplot(survfit.t6.a_dementia, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t6.a_dementia, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_dementia <-arrange_ggsurvplots(ggsurv.t6.a_dementia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_dementia

Heart disease

survfit.t6.a_heart_disease<-survfit(Surv(time, status) ~ a_heart_disease, 
                 data = r.hospitalised.death)
ggsurv.t6.a_heart_disease<- list(
  ggsurvplot(survfit.t6.a_heart_disease, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t6.a_heart_disease, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_heart_disease <-arrange_ggsurvplots(ggsurv.t6.a_heart_disease, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_heart_disease

Hyperlipidemia

survfit.t6.a_hyperlipidemia<-survfit(Surv(time, status) ~ a_hyperlipidemia, 
                 data = r.hospitalised.death)
ggsurv.t6.a_hyperlipidemia<- list(
  ggsurvplot(survfit.t6.a_hyperlipidemia, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t6.a_hyperlipidemia, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_hyperlipidemia <-arrange_ggsurvplots(ggsurv.t6.a_hyperlipidemia, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_hyperlipidemia

Hypertension

survfit.t6.a_hypertension<-survfit(Surv(time, status) ~ a_hypertension, 
                 data = r.hospitalised.death)
ggsurv.t6.a_hypertension<- list(
  ggsurvplot(survfit.t6.a_hypertension, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t6.a_hypertension, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_hypertension <-arrange_ggsurvplots(ggsurv.t6.a_hypertension, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_hypertension

Obesity

survfit.t6.a_obesity<-survfit(Surv(time, status) ~ a_obesity, 
                 data = r.hospitalised.death)
ggsurv.t6.a_obesity<- list(
  ggsurvplot(survfit.t6.a_obesity, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t6.a_obesity, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_obesity <-arrange_ggsurvplots(ggsurv.t6.a_obesity, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_obesity

Type 2 DM

survfit.t6.a_t2_diabetes<-survfit(Surv(time, status) ~ a_t2_diabetes, 
                 data = r.hospitalised.death)
ggsurv.t6.a_t2_diabetes<- list(
  ggsurvplot(survfit.t6.a_t2_diabetes, fun = "event", conf.int =TRUE),
  ggsurvplot(survfit.t6.a_t2_diabetes, fun = "cloglog", conf.int =TRUE))
ggsurv.t6.a_t2_diabetes <-arrange_ggsurvplots(ggsurv.t6.a_t2_diabetes, print = FALSE, ncol = 1, nrow = 2)
ggsurv.t6.a_t2_diabetes